TAT hitchhiker selection expanded to folding helpers, multimeric interactions and combinations with protein fragment complementation
Bibliographic record
Abstract
The twin-arginine translocation (TAT) pathway of the bacterial cytoplasmic membrane mediates translocation only of proteins that accomplished a native-like conformation. We deploy this feature in modular selection systems for directed evolution, in which folding helpers as well as dimeric or oligomeric protein-protein interactions enable TAT-dependent translocation of the resistance marker TEM β-lactamase (βL). Specifically, we demonstrate and analyze selection of (i) enhancers for folding by direct TAT translocation selection of a target protein interposed between the TorA signal sequence and βL, (ii) dimeric or oligomeric protein-protein interactions by hitchhiker translocation (HiT) selection of proteins fused to the TorA signal sequence and to the βL, respectively and (iii) heterotrimeric protein-protein interactions by combining HiT with protein fragment complementation selection of proteins fused to two split βL fragments and TorA, respectively. The lactamase fragments were additionally engineered for improved activity and stability. Applicability was benchmarked with interaction partners of known affinity and multimerization whereby cellular fitness correlated well with biophysical protein properties. Ultimately, the HiT selection was employed to identify peptides, which specifically bind to leukemia- and melanoma-relevant target proteins (MITF and ETO) by coiled-coil or tetra-helix-bundle formation with high affinity. The various versions of TAT selection led to inhibiting peptides (iPEPs) of disease-promoting interactions and enabled so far difficult to achieve selections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".